Search

Objectives: To maximise efficacy and minimise toxicity, special considerations are required for antibiotic prescription in elderly patients. This review aims to provide practical suggestions for the optimal management of antibiotic therapy in elderly patients., Methods: This was a narrative review. A literature search of published articles in the last 15 years on antibiotics and elderly patients was performed using the Cochrane Library and PubMed electronic databases. The three priority areas were identified: (i) pharmacokinetics/pharmacodynamics (PK/PD) for optimising dosage regimens and route of administration; (ii) antibiotic dosages in some special subpopulations; and (iii) treatment considerations relating to different antibiotic classes and their adverse events., Results: Clinicians should understand the altered PK/PD of drugs in this population owing to co-morbid conditions and normal physiological changes associated with ageing. The body of evidence justifies the need for individualised dose selection, especially in patients with impaired renal and liver function. Clinicians should be aware of the major drug-drug interactions commonly observed in the elderly as well as potential side effects., Conclusion: Antibiotic therapy in the elderly requires a comprehensive approach, including strategies to improve appropriate antibiotic prescribing, limit their use for uncomplicated infections and ensure the attainment of an optimal PK/PD target. To this purpose, further studies involving the elderly are needed to better understand the PK of antibiotics. Moreover, it is necessary to assess the role therapeutic drug monitoring in guiding antibiotic therapy in elderly patients in order to evaluate its impact on clinical outcome., (Copyright © 2020. Published by Elsevier Ltd.)

Purpose: An item response theory (IRT) pharmacometric framework is presented to characterize Functional Assessment of Cancer Therapy-Breast (FACT-B) data in locally-advanced or metastatic breast cancer patients treated with ado-trastuzumab emtansine (T-DM1) or capecitabine-plus-lapatinib., Methods: In the IRT model, four latent well-being variables, based on FACT-B general subscales, were used to describe the physical, social/family, emotional and functional well-being. Each breast cancer subscale item was reassigned to one of the other subscales. Longitudinal changes in FACT-B responses and covariate effects were investigated., Results: The IRT model could describe both item-level and subscale-level FACT-B data. Non-Asian patients showed better baseline social/family and functional well-being than Asian patients. Moreover, patients with Eastern Cooperative Oncology Group performance status of 0 had better baseline physical and functional well-being. Well-being was described as initially increasing or decreasing before reaching a steady-state, which varied substantially between patients and subscales. T-DM1 exposure was not related to any of the latent variables. Physical well-being worsening was identified in capecitabine-plus-lapatinib-treated patients, whereas T-DM1-treated patients typically stayed stable., Conclusion: The developed framework provides a thorough description of FACT-B longitudinal data. It acknowledges the multi-dimensional nature of the questionnaire and allows covariate and exposure effects to be evaluated on responses.

Background: Colistin-carbapenem combinations are synergistic in vitro against carbapenem-resistant Gram-negative bacteria. We aimed to test whether combination therapy improves clinical outcomes for adults with infections caused by carbapenem-resistant or carbapenemase-producing Gram-negative bacteria., Methods: A randomised controlled superiority trial was done in six hospitals in Israel, Greece, and Italy. We included adults with bacteraemia, ventilator-associated pneumonia, hospital-acquired pneumonia, or urosepsis caused by carbapenem-non-susceptible Gram-negative bacteria. Patients were randomly assigned (1:1) centrally, by computer-generated permuted blocks stratified by centre, to intravenous colistin (9-million unit loading dose, followed by 4·5 million units twice per day) or colistin with meropenem (2-g prolonged infusion three times per day). The trial was open-label, with blinded outcome assessment. Treatment success was defined as survival, haemodynamic stability, improved or stable Sequential Organ Failure Assessment score, stable or improved ratio of partial pressure of arterial oxygen to fraction of expired oxygen for patients with pneumonia, and microbiological cure for patients with bacteraemia. The primary outcome was clinical failure, defined as not meeting all success criteria by intention-to-treat analysis, at 14 days after randomisation. This trial is registered at ClinicalTrials.gov, number NCT01732250, and is closed to accrual., Findings: Between Oct 1, 2013, and Dec 31, 2016, we randomly assigned 406 patients to the two treatment groups. Most patients had pneumonia or bacteraemia (355/406, 87%), and most infections were caused by Acinetobacter baumannii (312/406, 77%). No significant difference between colistin monotherapy (156/198, 79%) and combination therapy (152/208, 73%) was observed for clinical failure at 14 days after randomisation (risk difference -5·7%, 95% CI -13·9 to 2·4; risk ratio [RR] 0·93, 95% CI 0·83-1·03). Results were similar among patients with A baumannii infections (RR 0·97, 95% CI 0·87-1·09). Combination therapy increased the incidence of diarrhoea (56 [27%] vs 32 [16%] patients) and decreased the incidence of mild renal failure (37 [30%] of 124 vs 25 [20%] of 125 patients at risk of or with kidney injury)., Interpretation: Combination therapy was not superior to monotherapy. The addition of meropenem to colistin did not improve clinical failure in severe A baumannii infections. The trial was unpowered to specifically address other bacteria., Funding: EU AIDA grant Health-F3-2011-278348., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Design of phase 1 combination therapy trials is complex compared to single therapy trials. In this work, model-based adaptive optimal design (MBAOD) was exemplified and evaluated for a combination of paclitaxel and a hypothetical new compound in a phase 1 study to determine the best dosing regimen for a phase 2 trial. Neutropenia was assumed as the main toxicity and the dose optimization process targeted a 33% probability of grade 4 neutropenia and maximal efficacy (based on preclinical studies) by changing the dose amount of both drugs and the dosing schedule for the new drug. Different starting conditions (e.g., initial dose), search paths (e.g., maximal change in dose intensity per step), and stopping criteria (e.g., "3 + 3 rule") were explored. The MBAOD approach was successfully implemented allowing the possibility of flexible designs with the modification of doses and dosing schedule throughout the trial. The 3 + 3 rule was shown to be highly conservative (selection of a dosing regimen with at least 90% of the possible maximal efficacy in less than 21% of the cases) but also safer (selection of a toxic design in less than 2% of the cases). Without the 3 + 3 rule, better performance was observed (>67% of selected designs were associated with at least 90% of possible maximal efficacy) while the proportion of DLTs per trial was similar. Overall, MBAOD is a promising tool in the context of dose finding studies of combination treatments and was showed to be flexible enough to be associated with requirements imposed by clinical protocols.

Background: Pharmacokinetic/pharmacodynamic (PKPD) models developed based on data from in vitro time-kill experiments have been suggested to contribute to more efficient drug development programmes and better dosing strategies for antibiotics. However, for satisfactory predictions such models would have to show good extrapolation properties., Objectives: To evaluate if a previously described mechanism-based PKPD model was able also to predict drug efficacy for higher bacterial densities and across bacterial strains., Methods: A PKPD model describing the efficacy of ciprofloxacin on Escherichia coli was evaluated. The predictive performance of the model was evaluated across several experimental conditions with respect to: (i) bacterial start inoculum ranging from the standard of ∼106 cfu/mL up to late stationary-phase cultures; and (ii) efficacy for seven additional strains (three laboratory and four clinical strains), not included during the model development process, based only on information regarding their MIC. Model predictions were performed according to the intended experimental protocol and later compared with observed bacterial counts., Results: The mechanism-based PKPD model structure developed based on data from standard start inoculum experiments was able to accurately describe the inoculum effect. The model successfully predicted the time course of drug efficacy for additional laboratory and clinical strains based on only the MIC values. The model structure was further developed to better describe the stationary phase data., Conclusions: This study supports the use of mechanism-based PKPD models based on preclinical data for predictions of untested scenarios., (© The Author 2017. Published by Oxford University Press on behalf of the British Society for Antimicrobial Chemotherapy. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

The purpose of this study was to develop a whole-body physiologically based pharmacokinetic (WB-PBPK) model for ciprofloxacin for ICU patients, based on only plasma concentration data. In a next step, tissue and organ concentration time profiles in patients were predicted using the developed model. The WB-PBPK model was built using a non-linear mixed effects approach based on data from 102 adult intensive care unit patients. Tissue to plasma distribution coefficients (Kp) were available from the literature and used as informative priors. The developed WB-PBPK model successfully characterized both the typical trends and variability of the available ciprofloxacin plasma concentration data. The WB-PBPK model was thereafter combined with a pharmacokinetic-pharmacodynamic (PKPD) model, developed based on in vitro time-kill data of ciprofloxacin and Escherichia coli to illustrate the potential of this type of approach to predict the time-course of bacterial killing at different sites of infection. The predicted unbound concentration-time profile in extracellular tissue was driving the bacterial killing in the PKPD model and the rate and extent of take-over of mutant bacteria in different tissues were explored. The bacterial killing was predicted to be most efficient in lung and kidney, which correspond well to ciprofloxacin's indications pneumonia and urinary tract infections. Furthermore, a function based on available information on bacterial killing by the immune system in vivo was incorporated. This work demonstrates the development and application of a WB-PBPK-PD model to compare killing of bacteria with different antibiotic susceptibility, of value for drug development and the optimal use of antibiotics .

Purpose: Antibiotic dose predictions based on PK/PD indices rely on that the index type and magnitude is insensitive to the pharmacokinetics (PK), the dosing regimen, and bacterial susceptibility. In this work we perform simulations to challenge these assumptions for meropenem and Pseudomonas aeruginosa., Methods: A published murine dose fractionation study was replicated in silico. The sensitivity of the PK/PD index towards experimental design, drug susceptibility, uncertainty in MIC and different PK profiles was evaluated., Results: The previous murine study data were well replicated with fT > MIC selected as the best predictor. However, for increased dosing frequencies fAUC/MIC was found to be more predictive and the magnitude of the index was sensitive to drug susceptibility. With human PK fT > MIC and fAUC/MIC had similar predictive capacities with preference for fT > MIC when short t1/2 and fAUC/MIC when long t1/2., Conclusions: A longitudinal PKPD model based on in vitro data successfully predicted a previous in vivo study of meropenem. The type and magnitude of the PK/PD index were sensitive to the experimental design, the MIC and the PK. Therefore, it may be preferable to perform simulations for dose selection based on an integrated PK-PKPD model rather than using a fixed PK/PD index target.

Inter occasion variability (IOV) is of importance to consider in the development of a design where individual pharmacokinetic or pharmacodynamic parameters are of interest. IOV may adversely affect the precision of maximum a posteriori (MAP) estimated individual parameters, yet the influence of inclusion of IOV in optimal design for estimation of individual parameters has not been investigated. In this work two methods of including IOV in the maximum a posteriori Fisher information matrix (FIMMAP) are evaluated: (i) MAP occ-the IOV is included as a fixed effect deviation per occasion and individual, and (ii) POP occ-the IOV is included as an occasion random effect. Sparse sampling schedules were designed for two test models and compared to a scenario where IOV is ignored, either by omitting known IOV (Omit) or by mimicking a situation where unknown IOV has inflated the IIV (Inflate). Accounting for IOV in the FIMMAP markedly affected the designs compared to ignoring IOV and, as evaluated by stochastic simulation and estimation, resulted in superior precision in the individual parameters. In addition MAPocc and POP occ accurately predicted precision and shrinkage. For the investigated designs, the MAP occ method was on average slightly superior to POP occ and was less computationally intensive.

Purpose: To characterize the population pharmacokinetics of bevacizumab, its binding properties to VEGF165 and the effect of demographic data and VEGF-A polymorphisms on the interplay between bevacizumab serum pharmacokinetics and VEGF165 serum concentrations in patients with colorectal cancer stage IV., Methods: Bevacizumab and VEGF165 data were collected from 19 adult patients with metastatic colorectal cancer enrolled in an observational clinical study. Bevacizumab was administered with one of the following combinations: 5-FU/Leucovorin/Irinotecan, 5-FU/Leucovorin/Oxaliplatin, Capecitabine/Irinotecan at doses ranging from 5 to 10 mg/kg every 2 or 3 weeks. Data analysis was performed using nonlinear mixed-effects modeling implemented in NONMEM 7.3., Results: A target-mediated drug disposition model adequately described bevacizumab concentration changes over time and its binding characteristics to VEGF165. The estimated clearance of bevacizumab was 0.18 L/day, the free VEGF165 levels at baseline were 212 ng/L, and the elimination rate constant of free VEGF165 was 0.401 day(-1). Body weight was allometrically included in all PK parameters., Conclusion: The final model adequately described the pre- and post-dose concentrations of total bevacizumab and free VEGF165 in patients with colorectal cancer. Model parameters were consistent with those previously reported for patients with solid tumors. Correlations between the binding affinity of bevacizumab and the VEGF-2578C/A and VEGF-634G/C polymorphisms were noticed.

Informative dropout can lead to bias in statistical analyses if not handled appropriately. The objective of this simulation study was to investigate the performance of nonlinear mixed effects models with regard to bias and precision, with and without handling informative dropout. An efficacy variable and dropout depending on that efficacy variable were simulated and model parameters were reestimated, with or without including a dropout model. The Laplace and FOCE-I estimation methods in NONMEM 7, and the stochastic simulations and estimations (SSE) functionality in PsN, were used in the analysis. For the base scenario, bias was low, less than 5% for all fixed effects parameters, when a dropout model was used in the estimations. When a dropout model was not included, bias increased up to 8% for the Laplace method and up to 21% if the FOCE-I estimation method was applied. The bias increased with decreasing number of observations per subject, increasing placebo effect and increasing dropout rate, but was relatively unaffected by the number of subjects in the study. This study illustrates that ignoring informative dropout can lead to biased parameters in nonlinear mixed effects modeling, but even in cases with few observations or high dropout rate, the bias is relatively low and only translates into small effects on predictions of the underlying effect variable. A dropout model is, however, crucial in the presence of informative dropout in order to make realistic simulations of trial outcomes.

Purpose: Neutropenia is a severe adverse-event of chemotherapeutics. Neutrophils (ANC) are mainly regulated by granulocyte colony stimulating factor (G-CSF). The aim was to characterize the dynamics between endogenous G-CSF and ANC over time following chemotherapy., Methods: Endogenous G-CSF and ANC were monitored in forty-nine breast cancer patients treated with sequential adjuvant 5-fluorouracil-epirubicin-cyclophosphamide and docetaxel., Results: During treatment courses ANC was transiently decreased and was reflected in an endogenous G-CSF increase, which was well described by a semi-mechanistic model including control mechanisms; when G-CSF concentrations increased the proliferation rate increased and the bone maturation time reduced for ANC. Subsequently, ANC in the circulation increased leading to increased elimination of G-CSF. Additionally, a non-specific elimination for G-CSF was quantified. The ANC-dependent elimination contributed to 97% at baseline and 49% at an ANC of 0.1 · 10(9)/L to the total G-CSF elimination., Conclusion: The integrated G-CSF-myelosuppression model captured the initial rise in endogenous G-CSF following chemotherapy-induced neutropenia and the return to baseline of G-CSF and ANC. The model supported the self-regulatory properties of the system and may be a useful tool for further characterization of the biological system and in optimization of chemotherapy treatment.

Trastuzumab emtansine (T-DM1) is an antibody-drug conjugate (ADC) therapeutic for treatment of human epidermal growth factor receptor 2 (HER2)-positive cancers. The T-DM1 dose product contains a mixture of drug-to-antibody ratio (DAR) moieties whereby the small molecule DM1 is chemically conjugated to trastuzumab antibody. The pharmacokinetics (PK) underlying this system and other ADCs are complex and have not been elucidated. Accordingly, we have developed two PK modeling approaches from preclinical data to conceptualize and understand T-DM1 PK, to quantify rates of DM1 deconjugation, and to elucidate the link between trastuzumab, T-DM1, and DAR measurements. Preclinical data included PK studies in rats (n = 34) and cynomolgus monkeys (n = 18) at doses ranging from 0.3 to 30 mg/kg and in vitro plasma stability. T-DM1 and total trastuzumab (TT) plasma concentrations were measured by enzyme-linked immunosorbent assay. Individual DAR moieties were measured by affinity capture liquid chromatography-mass spectrophotometry. Two PK modeling approaches were developed for T-DM1 using NONMEM 7.2 software: a mechanistic model fit simultaneously to TT and DAR concentrations and a reduced model fit simultaneously to TT and T-DM1 concentrations. DAR moieties were well described with a three-compartmental model and DM1 deconjugation in the central compartment. DM1 deconjugated fastest from the more highly loaded trastuzumab molecules (i.e., DAR moieties that are ≥3 DM1 per trastuzumab). T-DM1 clearance (CL) was 2-fold faster than TT CL due to deconjugation. The two modeling approaches provide flexibility based on available analytical measurements for T-DM1 and a framework for designing ADC studies and PK-pharmacodynamic modeling of ADC efficacy- and toxicity-related endpoints.

Purpose: Numerous studies have investigated causes of warfarin dose variability in adults, whereas studies in children are limited both in numbers and size. Mechanism-based population modelling provides an opportunity to condense and propagate prior knowledge from one population to another. The main objectives with this study were to evaluate the predictive performance of a theoretically bridged adult warfarin model in children, and to compare accuracy in dose prediction relative to published warfarin algorithms for children., Method: An adult population pharmacokinetic/pharmacodynamic (PK/PD) model for warfarin, with CYP2C9 and VKORC1 genotype, age and target international normalized ratio (INR) as dose predictors, was bridged to children using allometric scaling methods. Its predictive properties were evaluated in an external data set of children 0-18 years old, including comparison of dose prediction accuracy with three pharmacogenetics-based algorithms for children., Results: Overall, the bridged model predicted INR response well in 64 warfarin-treated Swedish children (median age 4.3 years), but with a tendency to overpredict INR in children ≤2 years old. The bridged model predicted 20 of 49 children (41 %) within ± 20 % of actual maintenance dose (median age 7.2 years). In comparison, the published dosing algorithms predicted 33-41 % of the children within ±20 % of actual dose. Dose optimization with the bridged model based on up to three individual INR observations increased the proportion within ±20 % of actual dose to 70 %., Conclusion: A mechanism-based population model developed on adult data provides a promising first step towards more individualized warfarin therapy in children.

Prolactin elevation is a side effect of all currently available D(2) receptor antagonists used in the treatment of schizophrenia. Prolactin elevation is the result of a direct antagonistic D(2) effect blocking the tonic inhibition of prolactin release by dopamine. The aims of this work were to assess the correlation between in vitro estimates of D(2) receptor affinity and pharmacokinetic-pharmacodynamic model-based estimates obtained from analysis of clinical data using an agonist-antagonist interaction (AAI) model and to assess the value of such a correlation in early prediction of full prolactin time profiles. A population model describing longitudinal prolactin data was fitted to clinical data from 16 clinical phases 1 and 3 trials including five different compounds. Pharmacokinetic data were modeled for each compound and the prolactin model was both fitted in per-compound fits as well as simultaneously to all prolactin data. Estimates of prolactin elevating potency were compared to corresponding in vitro values and their predictability was evaluated through model-based simulations. The model successfully described the prolactin time course for all compounds. Estimates derived from experimental preclinical data and the model fit of the clinical data were strongly correlated (p<0.001), and simulations adequately predicted the prolactin elevation in five out of six compounds. The AAI model has the potential to be used in drug development to predict prolactin response for a given exposure of D(2) antagonists using routinely produced preclinical data.

Purpose: Trastuzumab emtansine (T-DM1) is an antibody-drug conjugate in the development for the treatment of human epidermal growth factor receptor 2-positive cancers. Thrombocytopenia (TCP) is the dose-limiting toxicity of T-DM1. A semimechanistic population pharmacokinetic/pharmacodynamic (PK/PD) model was developed to characterize the effect of T-DM1 on patient platelet counts., Methods: A PK/PD model with transit compartments that mimic platelet development and circulation was fit to concentration-platelet-time course data from two T-DM1 single-agent studies (TDM3569g; N = 52 and TDM4258g; N = 112). NONMEM(®) 7 software was used for model development. Data from a separate phase II study (TDM4374g; N = 110) were used for model evaluation. Patient baseline characteristics were evaluated as covariates of model PD parameters., Results: The model described the platelet data well and predicted the incidence of grade ≥3 TCP. The model predicted that with T-DM1 3.6 mg/kg given every 3 weeks (q3w), the lowest platelet nadir would occur after the first dose. Also predicted was a patient subgroup (46 %) having variable degrees of downward drifting platelet-time profiles, which were predicted to stabilize by the eighth treatment cycle to platelet counts above grade 3 TCP. Baseline characteristics were not significant covariates of PD parameters in the model., Conclusions: This semimechanistic PK/PD model accurately captures the cycle 1 platelet nadir, the downward drift noted in some patient platelet-time profiles, and the ~8 % incidence of grade ≥3 TCP with T-DM1 3.6 mg/kg q3w. This model supports T-DM1 3.6 mg/kg q3w as a well-tolerated dose with minimal dose delays or reductions for TCP.

Purpose: Interpatient variability in the pharmacokinetics (PK) of cytarabine, etoposide, and daunorubicin following body surface area-adjusted doses calls for studies that point to other covariates to explain this variability. The purpose of this study was to investigate such relationships and give insights into the PK of this combination treatment., Methods: A prospective population PK study of twenty-three patients with acute myeloid leukemia was undertaken. Plasma concentrations of patients were determined by high-pressure liquid chromatography. PK models were developed with NONMEM; for daunorubicin, PK information from a prior study was utilized., Results: Baseline white blood cell count (bWBC) influenced the PK for all drugs. A small, statistically insignificant improvement in model fit was achieved when a relationship between bWBC and daunorubicin central volume of distribution was included. The volume increased 1.9% for each increase in bWBC by 1 × 10(6) cells/mL. The clearances of etoposide and cytarabine were significantly increased and decreased, respectively, by increased bWBC. Tenfold changes in bWBC were needed for these relationships to have potential clinical relevance. A decrease in creatinine clearance of 60 mL/min resulted in a decrease in etoposide clearance of 32%., Conclusions: Population-based models characterized the PK for all three drugs. bWBC was a significant covariate for etoposide and cytarabine and showed a trend for daunorubicin. Linking the significant bWBC relationships and the relationship between kidney function and etoposide clearance to clinical end points would support dose individualization. Patients with above-normal creatinine clearances and high bWBC may receive sub-optimal treatment due to elevated etoposide clearances.

Purpose: Chemotherapy-induced neutropenia is associated with the risk of developing febrile neutropenia (FN). The aim was to describe the time course of myelosuppression in breast cancer patients treated with docetaxel and to investigate how the shape of the predicted myelosuppression time course and earlier proposed risk factors influence the probability of developing FN., Methods: Neutrophil counts from 140 breast cancer patients with observed grade IV neutropenia during the first course of docetaxel treatment were included. Twenty-six of the patients (19%) experienced FN. The myelosuppression time course was described using a semi-mechanistic myelosuppression model in NONMEM. The individual myelosuppression model parameters [baseline neutrophil count, mean transit time (MTT) and drug effect parameter (EC(50))], myelosuppression descriptors (nadir, duration of grade IV neutropenia) and earlier suggested risk factors (age, performance status, haemoglobin and liver function) were explored to be related to FN by logistic regression., Results: The neutrophil time course following docetaxel treatment was well described by the model. EC(50) and MTT were both significantly related to the probability of developing FN where low parameter values result in a rapid decline, low nadir and an increased risk of FN. None of the evaluated risk factors or myelosuppression descriptors were significant., Conclusion: The probability to develop FN in patients who experience grade IV neutropenia was dependent on the myelosuppression time profile. Patients with a rapid neutrophil decline and high drug sensitivity had a higher probability to develop FN. Model-based parameter estimates were superior predictors over descriptive values such as the nadir or duration of neutropenia.

In cancer chemotherapy neutropenia is a common dose-limiting toxicity. An ability to predict the neutropenic effects of cytotoxic agents based on proposed trial designs and models conditioned on previous studies would be valuable. The aim of this study was to evaluate the ability of a semi-mechanistic pharmacokinetic/pharmacodynamic (PK/PD) model for myelosuppression to predict the neutropenia observed in Phase I clinical studies, based on parameter estimates obtained from prior trials. Pharmacokinetic and neutropenia data from 5 clinical trials for diflomotecan and from 4 clinical trials for indisulam were used. Data were analyzed and simulations were performed using the population approach with NONMEM VI. Parameter sets were estimated under the following scenarios: (a) data from each trial independently, (b) pooled data from all clinical trials and (c) pooled data from trials performed before the tested trial. Model performance in each of the scenarios was evaluated by means of predictive (visual and numerical) checks. The semi-mechanistic PK/PD model for neutropenia showed adequate predictive ability for both anti-cancer agents. For diflomotecan, similar predictions were obtained for the three scenarios. For indisulam predictions were better when based on data from the specific study, however when the model parameters were conditioned on data from trials performed prior to a specific study, similar predictions of the drug related-neutropenia profiles and descriptors were obtained as when all data were used. This work provides further indication that modeling and simulation tools can be applied in the early stages of drug development to optimize future trials.

Aim: To investigate the potential of a model for chemotherapy-induced myelosuppression to predict the full time-course of myelosuppression in patients based on rat data., Methods: White blood cell counts were determined in rats after administration of 5-fluorouracil, epirubicin, cyclophosphamide, docetaxel, paclitaxel or etoposide. Pharmacokinetic models were used to predict the concentration-time profile in each rat. A semi-physiological model of myelosuppression was applied to the rat data. The drug-related parameter Slope was allowed to differ between drugs. The analysis was performed in NONMEM VI. Time-courses of myelosuppression in patients were predicted for each drug based on patient pharmacokinetic models, typical system-related parameters previously determined in patients and the rat Slope estimates in the present study., Results: The semi-physiological model of myelosuppression fit the rat data well and the estimated maturation time in rats (53 h) was approximately half of the previous estimate in patients. The relative difference in Slope estimates for rats and patients based on total drug concentrations ranged between 28% to 8-fold for the six drugs. The differences reduced to 8-37% for all drugs when correcting the rat Slope estimates for species difference in protein binding and in CFU-GM assay sensitivity., Conclusions: This method for interspecies scaling was successful in predicting the time-course of myelosuppression in patients based on rat data. Predictions improved when species differences in protein binding and CFU-GM assay sensitivity were accounted for. The approach appears promising for predicting myelosuppression in patients early in development.